Known, in particular in the medical field, are prediction devices based on multivariable predictive functions, such as classifiers, which make it possible to predict information on all of the observable traits that characterize living beings, such as anatomical, morphological, molecular, physiological or ethological traits, or others.
A phenotype to be predicted can also be a clinical diagnosis, for example sick/not sick, or a patient's response to medical treatment.
The prediction thus relates to phenotype information that can be of different natures, e.g. biological, clinical (response to treatment, illness diagnosis, etc.) or demographic (age, sex, etc.).
The “phenotype” of a person therefore refers to any biological, clinical or demographic trait of that person.
However, such prediction devices are generally faced with what is commonly called the “curse of dimensionality,” which is a well-known problem that amounts to drawing conclusions from a reduced number of observations in a large input data, or descriptor, space, which leads to poor performance of the prediction device.